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首页> 外文会议>Annual meeting of the Decision Sciences Institute >CLASSIFICATION MODELS FOR PREDICTING TRANSPORTATION MODE CHOICE FOR COMMUTERS IN A MAJOR METROPOLITAN AREA
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CLASSIFICATION MODELS FOR PREDICTING TRANSPORTATION MODE CHOICE FOR COMMUTERS IN A MAJOR METROPOLITAN AREA

机译：预测大都市地区通勤者运输方式选择的分类模型

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This study investigates the problem of classifying commuters in a metropolitan area into groups differentiated by the mode of transportation selected. Typically, urban planners have used the logistic model for this task. To gain insight into how alternative classification models perform, six classification models are compared using approximately ten thousand person trip observations from the Dallas/Ft. Worth metroplex. The classification models include the linear and quadratic discriminant procedures, the logit model, and three neural network models. The study revealed that the modular neural network model was a viable alternative to the traditional approach to predicting transportation mode choice.
机译：这项研究调查了将大都市地区的通勤者分类为按所选择的交通方式区分的群体的问题。通常，城市规划人员已将物流模型用于此任务。为了深入了解替代分类模型的性能，使用来自达拉斯/英尺的大约一万人的出行观察对六个分类模型进行了比较。值得大都会。分类模型包括线性和二次判别过程，logit模型和三个神经网络模型。研究表明，模块化神经网络模型是预测运输方式选择的传统方法的可行替代方案。

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